Applied Computing and Intelligence最新文献

Novel split quality measures for stratified multilabel cross validation with application to large and sparse gene ontology datasets 应用于大型稀疏基因本体数据集的分层多标签交叉验证的新分离质量度量

Applied Computing and Intelligence Pub Date : 2021-09-03 DOI: 10.3934/aci.222003

Henri Tiittanen, L. Holm, Petri Toronen

引用次数: 3

Crop and weed classification based on AutoML 基于 AutoML 的作物和杂草分类

Applied Computing and Intelligence Pub Date : 2020-10-28 DOI: 10.3934/aci.2021003

Xuetao Jiang, Binbin Yong, Soheila Garshasbi, Jun Shen, Meiyu Jiang, Qingguo Zhou

引用次数: 0

All-pairwise squared distances lead to more balanced clustering 全成对平方距离导致更平衡的聚类

Applied Computing and Intelligence Pub Date : 1900-01-01 DOI: 10.3934/aci.2023006

Mikko I. Malinen, P. Fränti

引用次数: 1

Investigation of ant cuticle dataset using image texture analysis 基于图像纹理分析的蚂蚁角质层数据研究

Applied Computing and Intelligence Pub Date : 1900-01-01 DOI: 10.3934/aci.2022008

Noah Gardner, John Paul Hellenbrand, Anthony Phan, Haige Zhu, Z. Long, Min Wang, C. Penick, Chih-Cheng Hung

引用次数: 0

A comprehensive survey of zero-shot image classification: methods, implementation, and fair evaluation 零拍图像分类:方法、实现与公平评价综述

Applied Computing and Intelligence Pub Date : 1900-01-01 DOI: 10.3934/aci.2022001

Guanyu Yang, Zihan Ye, Rui Zhang, Kaizhu Huang

引用次数: 3

State estimation and optimal control of an inverted pendulum on a cart system with stochastic approximation approach 推车系统倒立摆状态估计与最优控制的随机逼近方法

Applied Computing and Intelligence Pub Date : 1900-01-01 DOI: 10.3934/aci.2023005

Xian Wen Sim, S. Kek, Sy Yi Sim

{"title":"State estimation and optimal control of an inverted pendulum on a cart system with stochastic approximation approach","authors":"Xian Wen Sim, S. Kek, Sy Yi Sim","doi":"10.3934/aci.2023005","DOIUrl":"https://doi.org/10.3934/aci.2023005","url":null,"abstract":"\u0000 In this paper, optimal control of an inverted pendulum on a cart system is studied. Since the nonlinear structure of the system is complex, and in the presence of random disturbances, optimization and control of the motion of the system become more challenging. For handling this system, a discrete-time stochastic optimal control problem for the system is described, where the external force is considered as the control input. By defining a loss function, namely, the mean squared errors to be minimized, the stochastic approximation (SA) approach is applied to estimate the state dynamics. In addition, the Hamiltonian function is defined, and the first-order necessary conditions are derived. The gradient of the cost function is determined so that the SA approach is employed to update the control sequences. For illustration, considering the values of the related parameters in the system, the discrete-time stochastic optimal control problem is solved iteratively by using the SA algorithm. The simulation results show that the state estimation and the optimal control law design are well performed with the SA algorithm, and the motion of the inverted pendulum cart is addressed satisfactorily. In conclusion, the efficiency of the SA approach for solving the inverted pendulum on a cart system is verified.\u0000","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130924268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perceptual loss function for generating high-resolution climate data 用于生成高分辨率气候数据的感知损失函数

Applied Computing and Intelligence Pub Date : 1900-01-01 DOI: 10.3934/aci.2022009

Yang Wang, H. Karimi

{"title":"Perceptual loss function for generating high-resolution climate data","authors":"Yang Wang, H. Karimi","doi":"10.3934/aci.2022009","DOIUrl":"https://doi.org/10.3934/aci.2022009","url":null,"abstract":"\u0000 When planning the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally significant systems, policy makers require accurate and high-resolution data reflecting different climate scenarios. There is widely documented evidence that perceptual loss can be used to generate perceptually realistic results when mapping low-resolution inputs to high-resolution outputs, but its application is limited to images at present. In this paper, we study the perceptual loss when increasing the resolution of raw precipitation data by ×4 and ×8 under training modes of CNN and GAN. We examine the difference in the perceptual loss calculated by using different layers of feature maps and demonstrate how low- and mid-level feature maps can yield comparable results to pixel-wise loss. In particular, from both qualitative and quantitative points of view, Conv2_1 and Conv3_1 are the best compromises between obtaining detailed information and maintaining the overall error in our case. We propose a new approach to benefit from perceptual loss while considering the characteristics of climate data. We show that in comparison to calculating perceptual loss directly for the entire sample, our proposed approach can obtain detailed information of extreme events regions while reducing error.\u0000","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128197874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A review of the application of machine learning in adult obesity studies 机器学习在成人肥胖研究中的应用综述

Applied Computing and Intelligence Pub Date : 1900-01-01 DOI: 10.3934/aci.2022002

M. Alkhalaf, Ping Yu, Jun Shen, Chao Deng

{"title":"A review of the application of machine learning in adult obesity studies","authors":"M. Alkhalaf, Ping Yu, Jun Shen, Chao Deng","doi":"10.3934/aci.2022002","DOIUrl":"https://doi.org/10.3934/aci.2022002","url":null,"abstract":"\u0000 In obesity studies, several researchers have been applying machine learning tools to identify factors affecting human body weight. However, a proper review of strength, limitations and evaluation metrics of machine learning algorithms in obesity is lacking. This study reviews the status of application of machine learning algorithms in obesity studies and to identify strength and weaknesses of these methods. A scoping review of paper focusing on obesity was conducted. PubMed and Scopus databases were searched for the application of machine learning in obesity using different keywords. Only English papers in adult obesity between 2014 and 2019 were included. Also, only papers that focused on controllable factors (e.g., nutrition intake, dietary pattern and/or physical activity) were reviewed in depth. Papers on genetic or childhood obesity were excluded. Twenty reviewed papers used machine learning algorithms to identify the relationship between the contributing factors and obesity. Regression algorithms were widely applied. Other algorithms such as neural network, random forest and deep learning were less exploited. Limitations regarding data priori assumptions, overfitting and hyperparameter optimization were discussed. Performance metrics and validation techniques were identified. Machine learning applications are positively impacting obesity research. The nature and objective of a study and available data are key factors to consider in selecting the appropriate algorithms. The future research direction is to further explore and take advantage of the modern methods, i.e., neural network and deep learning, in obesity studies.\u0000","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114683803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Puzzle-Mopsi: a location-puzzle game Puzzle-Mopsi:一款位置解谜游戏

Applied Computing and Intelligence Pub Date : 1900-01-01 DOI: 10.3934/aci.2023001

P. Fränti, Lingyi Kong

引用次数: 0

Adjustable mode ratio and focus boost search strategy for cat swarm optimization 猫群优化的可调模式比和聚焦增强搜索策略

Applied Computing and Intelligence Pub Date : 1900-01-01 DOI: 10.3934/aci.2021005

Pei-wei Tsai, Xingsi Xue, Jing Zhang, V. Istanda

引用次数: 0